DatriseAI-first ETL

Outreach Databricks SQL Warehouse

AI-first ETL from Outreach into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Outreach into Databricks SQL Warehouse

Datrise syncs Outreach's sequence activity, pipeline execution metrics, and sales engagement events into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Outreach: Sales execution platform for sequence activity and pipeline outcomes.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Outreach entities map to Databricks SQL Warehouse

Outreach entityDatabricks SQL Warehouse objectNotes
sequence activityoutreach_sequence_activityTIMESTAMP events
pipeline execution metricsoutreach_pipeline_execution_metricsid PK · linked to outreach_sequence_activity
sales engagement eventsoutreach_sales_engagement_eventsTIMESTAMP events

FAQ

How does Datrise handle Outreach's custom fields in Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the Outreach to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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